As storage and availability of data grows, a large amount of time is spent identifying data relationships for discovery of new topics. Conventionally, the discovery of new topics is oftentimes performed manually by repetitive work leading to wasting valuable time of users.
Information can have great value. Assembling and maintaining a database to store information involves real costs, such as the costs to acquire information, the costs associated with physical assets used to house, secure, and make the information available, and labor costs to manage the information.
As computer processors are becoming more powerful, it would be particularly useful to save the time that an individual conventionally spends discovering new topics and identifying relationship criteria with existing models, or between the source and the target.
Oftentimes there are simple transformations, or complex topic identification across a large corpus of documents from any subject domain, requiring a lot of user's time for discovery of relationships associated with existing data.
Thus, there is a need for a simple and flexible method which assists users in connection with performing automated discovery of new topics, employing a new topic database for comparison with the existing topics for new application environments.